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This paper aims to propose an integrated protection scheme for converters of a low-power, low-cost photovoltaic system. Power electronic converters use a variety of methods to limit overload and fault current. The use of insulated and non-insulated sensors along with additional circuits to detect and limit fault current can cause current to be limited or completely cut off before damage to semiconductor devices. In addition, fuses that have slower performance are used as backup for any type of protection.

First, all the candidate points for protection are investigated. In this paper, after examining the performance of glass fuses as linear resistors, they are used as a current feedback element. A simple, isolated and reliable circuit for fault detection at various points of the system has been proposed that can be implemented and operated in single shot or auto-reclose operating mode.

The experimental results of this circuit on a dc/dc converter and an H-bridge inverter show that it can cut off all instantaneous short circuit errors in less than 50 µs and prevent damage to the semiconductor switch.

In low-cost and low-power converters, it is usually not cost-effective to use complex and expensive devices. For this reason, these converters are more vulnerable to faults. On the other hand, in complex systems such as photovoltaics, several converters are used simultaneously in different parts, and the occurrence of a fault in each of them causes the whole system to fail.

The purpose of this paper is to obtain an integrated method for inter-turn short circuit fault detection for the cage-rotor induction machine (CRIM) considering saturation effect.

The magnetic equivalent circuit (MEC) is proposed for machine modeling and nonlinear B-H curve is considered for saturation effect. The machine has some differential equations which are converted to algebraic type by trapezoidal method. On the other hand, some nonlinear equations are present due to saturation effect. A set of nonlinear algebraic equation should be solved by numerical method. Therefore, the Newton-Raphson technique is used for equation solving during of the considered time step.

Generally, the operating point of electrical machines is close to the saturation zone due to designing considerations. Moreover, some current and torque harmonics will be produced due to time and space harmonics combination, which cannot be studied when saturation modeling is neglected. Considering both space and time harmonics, a method is proposed for inter-turn short circuit fault detection based on the stator current signatures and the machine performance is analyzed in healthy and faulty cases. In order to obtain the integrated method, two sample machines (two and also four-pole machines) are modeled and finally the accuracy of the proposed method is verified through the experimental results.

The calculations have been done in this work is limited to CRIM considering. However, the presented modeling method can be used for another types of electrical machines by some minor modifications.

Obtaining of an integrated formula for the inter-turn short circuit fault detection which has been presented for first time is the more advantages of present work. Moreover, in order to saturation effect considering, a new method is presented for solving of nonlinear equations which is another novelty of paper.

This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases.

To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set.

The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load.

The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.

Interlaminar short circuits in turbo generator stators can lead to local damage of the iron core. The purpose of this paper is to model an interlaminar short circuit diagnosis test on an existing structure.

This work presents the modeling of short‐circuited laminations in a stator yoke of a turbo‐generator. A 3D finite element model, associated to a homogenization technique, is used to calculate the short‐circuit current. The diagnosis test known as El Cid has been modelled as well.

Calculation results are compared with the experiment. The same tendency has been observed both in experimental and numerical results.

Additional calculations may be performed (parametric studies) in order to investigate El Cid measuring under different conditions (different material properties, fault position, size), which may lead to a better interpretation of the results.

Modelling of short circuit diagnosis tests under different conditions may help with the interpretation of measuring results, predicting the fault size/seriousness and location. So, only the concerned parts of the stator have to be disassembled and repaired/rebuilt.

It is not easy to model numerically a structure with a short circuit inside, since different dimensions are involved: the fault and the varnish between laminations are much smaller than the stator itself. Thus, homogenization techniques have been used to model the lamination stack region. The combination of this technique with the modelling of the El Cid test constitutes a tool to study this kind of fault and calculate its severity and location in a stator.

This study presents a new method for the detection of faults in large transformer cores. It is based on the analysis of leakage flux components in the vicinity of the sheet stack. The purpose of this study is to provide a nondestructive analysis tool for transformer cores during the assembly process to detect accidental defects such as inter-laminar short circuits.

The different components of the leakage flux allow localization of the fault in the stack and also permit to assess its severity. Out of the many kinds of defects which may appear in a transformer core, this method only detects those which actually cause an increase in the transformer’s global iron losses, which are thus the most detrimental.

The proposed method allows a more efficient control of the quality of the cores during their manufacturing process. Until now, it was only possible to know the quality of the core when the transformer was fully assembled.

The accuracy of the method depends on the size of the defect and may request many measurements to give usable information.

Controlling iron losses in a core during its construction avoids heavy dismantling operations, both financially and temporally.

This method can help transformer manufacturers optimize their building process. In addition, the method remains effective regardless of the size of the core considered.

On-time fault diagnosis in electrical machines is a critical issue, as it can prevent the development of fault and also reduce the repairing time and cost. In brushless synchronous generators, the significance of the fault diagnosis is even more because they are widely used to generate electrical power all around the world. Therefore, this study aims to propose a fault detection approach for the brushless synchronous generator. In this approach, a novel extension of Relief feature selection method is developed.

In this paper, by taking the advantages of the finite element method (FEM), a brushless synchronous machine is modeled to evaluate the machine performance under two conditions. These conditions include the normal condition of the machine and one diode open-circuit of the rotating rectifier. Therefore, the harmonic behavior of the terminal voltage of the machine is obtained under these situations. Then, the harmonic components are ranked by using the extension of Relief to extract the most appropriate components for fault detection. Therefore, a fault detection approach is proposed based on the ranked harmonic components and support vector machine classifier.

The proposed diagnosis approach is verified by using an experimental test. Results show that by this approach open-circuit fault on the diode rectifier can effectively be detected by the accuracy of 98.5% and by using five harmonic components of the terminal voltage [1].

In this paper, a novel feature selection method is proposed to select the most effective FFT components based on an extension of Relief method, and besides, FEM modeling of a brushless synchronous generator for normal and one diode open-circuit fault.

The purpose of this paper is to present the influence of inter-turn short circuit faults (ITSF) on electromagnetic vibration in high-voltage line-starting permanent magnet synchronous motor (HVLSPMSMS).

In this paper, the ampere–conductor wave model of HVLSPMSM after ITSF is established. Second, a mathematical model of the magnetic field after ITSF is established, and the influence law of the ITSF on the air-gap magnetic field is analyzed. Further, the mathematical expression of the electromagnetic force density is established based on the Maxwell tensor method. The impact of HVLSPMSM torque ripple frequency, radial electromagnetic force spatial–temporal distribution and rotor unbalanced magnetic tension force by ITSF is revealed. Finally, the electromagnetic–mechanical coupling model of HVLSPMSM is established, and the vibration spectra of the motor with different degrees of ITSF are solved by numerical calculation.

In this study, it is found that the 2np order flux density harmonics and (2 N + 1) p order electromagnetic forces are not generated when ITSF occurs in HVLSPMSM.

By analyzing the multi-harmonics of HVLSPMSM after ITSF, this paper provides a reliable method for troubleshooting from the perspective of vibration and torque fluctuation and rotor unbalanced electromagnetic force.

The aim of this paper is to propose the model for analyzing the electromagnetic performances of permanent magnet vernier machines (PMVMs) under healthy and faulty conditions.

The model uses interconnected reluctance network formed based on the geometrical approximations to predict magnetic performances of the machine. The network consists of several types of reluctances for modeling different parts of machine. Applying Kirchhoffs laws in the network and the machine windings, magnetic and electrical equations are obtained, respectively. To construct the model system of equations, the electrical equation is converted into algebraic form by using the trapezoidal technique. Moreover, the system of equations must be solved by Newton–Raphson method in each step-time because of considering the core saturation effect.

The proposed model is developed based on the modified magnetic equivalent circuit (MEC) method, in which the number of flux paths in different parts of the machine can be arbitrary selected. The saturation effect, skewed slots, the desired machine geometrical parameters and various winding arrangements are included in the proposed model; therefore, it can evaluate the time and space harmonics in modeling the PMVMs. Furthermore, a pattern for inter-turn fault detection is extracted from the stator current spectrum. Finally, 2 D-finite element method (FEM) and 3 D-FEM analysis are carried out to evaluate and verify the results of the proposed MEC model.

Generally, the element numbers have important role in modeling the machine and calculating its performance. Hence, the proposed MEC model’s capability to choose desired number of flux paths is advantage of this paper. Moreover, the developed MEC can be used for analyzing several electrical machines, including other types of vernier machines, with simple modification.

The purpose of this paper is to introduce an asymmetric structure of the magnetic equivalent circuit (MEC) for analysis of the linear induction machine (LIM) with an internal short circuit fault.

By applying a proper MEC to the LIM, a generalized relation for the inductance matrix of the machine can be directly determined. To evaluate the proposed model, the stator currents and the air-gap flux with the proposed technique are given and compared to the simulation and experimental results in the healthy and fault conditions.

The LIM is an axial flux machine with a wide range of applications in high-performance drives. Due to a well-tried effect of the first tooth and the last one (the end effect), the performance level of the LIM decreases. Also, the analysis of the linear machines in fault conditions illustrates more complexity compared to the rotary induction machine. However, the MEC is very simple, describing the behavior of the asymmetric electromechanical devices using the magnetic reluctance or the permeance of flux paths.

Using the proposed model, there would be some decrease in the complications of the LIM analysis in the asymmetrical conditions. Moreover, analyzing some of the characteristics of the LIM, such as turn-fault condition, it can be calculated with high accuracy.

This paper aims to investigate the efficacy of teeth flux sensors in detecting, locating and assessing the severity of short-circuit faults in the stator windings of induction machines.

The experimental study involves inducing short-circuit winding turn variations on the induction machine’s stator and continuously measuring the RMS values across teeth flux sensors. Two crucial steps are taken for machine diagnosis: measurements under load operating conditions for fault detection and measurements under no-load conditions to determine fault location and severity.

The experimental results demonstrate that the proposed approach using teeth flux sensors is reliable and effective in detecting, locating and evaluating the severity of stator winding faults.

While this study focuses on short-circuit faults, future research could explore other fault types and alternative sensor configurations to enhance the comprehensiveness of fault diagnosis.

The methodology outlined in this paper holds the potential to significantly reduce maintenance time and costs for induction machines, leading to substantial savings for companies.

This research contributes to the field by presenting an innovative approach that uses teeth flux sensors for a comprehensive fault diagnosis in induction machines. The originality lies in the effectiveness of this approach in providing reliable fault detection, location and severity evaluation.